else:
                        boxes = torch.zeros(0)
                        labels = torch.zeros(0)
                        scores = torch.zeros(0)

                        aboxes = torch.zeros(0)
                        alabels = torch.zeros(0)
                        ascores = torch.zeros(0)

                    filename = '_'.join(
                        trainset.ids[index[kk][jj % trainset.time_window]][-1])
                    print('name: {}, image size: {}x{}x{}'.format(
                        filename, *vis.shape))

                    draw_boxes(ax, np.array(vis_img), boxes, labels, scores,
                               OBJ_CLASSES)
                    plt.savefig(filename + '_vis.jpg')

                    draw_boxes(
                        ax, 255 *
                        seg[kk, jj %
                            trainset.time_window].squeeze().byte().numpy(),
                        boxes, labels, scores, OBJ_CLASSES)
                    plt.savefig(filename + '_seg.jpg')

                    # seg_img = Image.fromarray(seg[kk, jj% trainset.time_window].squeeze().byte().numpy(), mode='P')
                    # seg_img.putpalette(seg_palette)
                    # seg_img.save( filename + '_seg.png' )

                    if len(boxes):
                        pdb.set_trace()
Example #2
0
                            alabels = torch.zeros(0)
                            ascores = torch.zeros(0)

                    else:
                        boxes = torch.zeros(0)
                        labels = torch.zeros(0)
                        scores = torch.zeros(0)

                        aboxes = torch.zeros(0)
                        alabels = torch.zeros(0)
                        ascores = torch.zeros(0)

                    filename = '_'.join( trainset.ids[index[kk][jj % trainset.time_window]][-1] )
                    print('name: {}, image size: {}x{}x{}'.format(filename, *vis.shape))            
                                        
                    draw_boxes(ax, np.array(vis_img), boxes, labels, scores, OBJ_CLASSES)
                    plt.savefig( filename + '_vis.jpg' )

                    

                    # if len(boxes):
                    #     gen_mask( boxes ).save( filename + '_mask.png' )
                    #     pdb.set_trace()

                    # draw_boxes(ax, np.array(lwir_img), boxes, labels, scores, OBJ_CLASSES)
                    # plt.savefig( filename + '_lwir.jpg' )                    
                    
                    # draw_boxes(ax, np.array(lwir_img), aboxes, alabels, ascores)
                    # plt.savefig( filename + '_lwir_anchors.jpg' )

                pdb.set_trace()
Example #3
0
                # if any(anchor_labels>20):
                #     pdb.set_trace()

                anchor_hist += np.histogram(anchor_labels.numpy(),
                                            np.arange(0, num_anchors + 1))[0]

                if bSave:

                    filename = '_'.join(trainset.ids[index][-1])
                    print('name: {}, image size: {}x{}x{}'.format(
                        filename, *vis.shape))
                    boxes[:, (0, 2)] *= ori_size[1]
                    boxes[:, (1, 3)] *= ori_size[0]

                    draw_boxes(ax, vis, boxes, labels, scores, OBJ_CLASSES,
                               colormap)
                    plt.savefig(filename + '_vis.jpg')

                    draw_boxes(ax, lwir, boxes, labels, scores, OBJ_CLASSES,
                               colormap)
                    plt.savefig(filename + '_lwir.jpg')

                    aboxes = anchors[0]
                    aboxes[:, (0, 2)] *= ori_size[1]
                    aboxes[:, (1, 3)] *= ori_size[0]
                    # draw_boxes(ax, lwir, aboxes, anchors[1]+1, anchors[2])
                    draw_boxes(ax,
                               lwir,
                               aboxes,
                               anchor_labels,
                               anchors[2],